It is aware of NumPy arrays as typed memory regions and so can speed-upcode using NumPy arrays. Other, less well-typed code will be translatedto Python C-API calls effectively removing the "interpreter" but not removingthe dynamic indirection.

Numba is also not a tracing jit. It *compiles* your code before it getsrun either using run-time type information or type information you providein the decorator.

Numba is a mechanism for producing machine code from Python syntax and typeddata structures such as those that exist in NumPy.

The easiest way to install numba and get updates is by using the AnacondaDistribution: http://continuum.io/anacondace.html

Custom Python Environments==========================

If you're not using anaconda, you will need LLVM with RTTI enabled:

* Compile LLVM 3.2

See https://github.com/llvmpy/llvmpy for the most up-to-date instructions.

```bash $ wget http://llvm.org/releases/3.2/llvm-3.2.src.tar.gz $ tar zxvf llvm-3.2.src.tar.gz $ ./configure --enable-optimized --prefix=LLVM_BUILD_DIR $ # It is recommended to separate the custom build from the default system $ # package. $ # Be sure your compiler architecture is same as version of Python you will use $ # e.g. -arch i386 or -arch x86_64. It might be best to be explicit about this. $ REQUIRES_RTTI=1 make install```